基于滚动时域粒子群优化的视频去雾算法
作者:
作者单位:

浙江工业大学 信息工程学院

作者简介:

通讯作者:

中图分类号:

TP751

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Receding horizon particle swarm optimization algorithm for video defogging
Author:
Affiliation:

College of Information Engineering, ZheJiang University Of Technology

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    无人机视频由于拍摄的位置和场景不断移动,环境参数亦不断变化,以往针对固定场景的去雾方法不能达到最佳效果。为了使无人机视频去雾算法具有自适应性,提出基于滚动时域粒子群优化的视频去雾算法。将基于周期和事件混合驱动的滚动调度策略与粒子群算法(PSO)结合,对可调去雾参数进行滚动优化调整,当与上次优化的帧间隔数大于阈值或环境、场景发生改变时,则启动粒子群优化算法重新选取最佳去雾参数。针对无人机视频,分别应用本文算法和固定去雾参数算法进行了实验和对比分析,实验结果表明对于环境因素动态变化的视频,本文算法比固定去雾参数算法具有更好对比度和视觉效果.

    Abstract:

    The defogging algorithm developed for fix scenes can not achieve the best effects for videos shot by unmanned aerial vehicles (UAVs) since the locations, scenes and environments of UAVs keep varying. The video defogging algorithm based on receding horizon particle swarm optimization is proposed for adaptive defogging. The defogging parameter can be optimized by particle swarm optimization (PSO) algorithm which is scheduled by receding horizon strategy driven by time and events. The PSO algorithm is started to optimize the defogging parameter when environment or scene changes, or frame interval is over the threshold. The proposed video defogging algorithm is verified in experiments and compared with fixed parameter video defogging algorithm for UAV videos. The experimental results show that the proposed algorithm has better contrast and visual effect than fixed parameter algorithm for videos with environment variations.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2019-08-19
  • 最后修改日期:2020-07-04
  • 录用日期:2020-07-15
  • 在线发布日期:
  • 出版日期: